Sindagi et al. presented in [1]} another method for crowd density and count estimation called Contextual Pyramid CNN (CP-CNN). The authors explicitly incorporate global and local contextual information of crowd images. In particular, the proposed architecture consists of four modules: i) a cnn-based Global Context Estimator (GCE) to incorporate global context, trained to encode the context of an input image and to classify them into different density classes; ii) a Density Map Estimator (DME), which is a multi-column architecture-based cnn with appropriate max-pooling layers, used to transform the image into high-dimensional feature maps; iii) a Local Context Estimator cnn (LCE) trained on input image patches to encode local context information, aiming at guiding the DME to estimate better quality maps; iv) a Fusion-CNN (F-CNN) that fuses the contextual information obtained by LCE and GCE with the output of DME.
